Aggregate, extract, normalize financial plans from distributed planning systems. Reconcile with actuals. Analytics for performance management and future planning
- Lack of visibility into carrier logistics at a major US retailer hampered efforts to determine how to improve logistics financial planning, a billion dollar item, to align with the execution costs
- And whether the costs were in line with pre-determined agreements.
- There were three unknowns: a) if the shipping routes were being executed by the carriers per the execution plan; b) if the shipping routes billed by the carrier matched the ones in the execution system; and c) whether the invoices aligned to contracted rates.
- All of this information was distributed across 35 distribution centers and numerous IT systems in a combination of unstructured and structured formats.
- RAGE-AI Intelligent Machine to extract greater insights from the financial planning data, transportation data.
- Automated cost auditing applying machine learning to review tens of thousands of invoices and contracts to obtain origination of cost variances and the financial impact.
- Automated matching of data from invoices and contracts with the execution system to discover the actual costs, which was then used for financial planning purposes.
- Realized ~$40 million in cost leakage on a $600 million spend.
- By identifying gaps between planning and actual performance, the automated solution significantly improved the company's approach to outbound logistics cost auditing and integrated cost planning.